中国科学院机构知识库网格系统: 检索

Unleashing the full potential of hyperspectral imaging: Decoupled image and frequency-domain spatial-spectral framework 期刊论文 OAI收割
EXPERT SYSTEMS WITH APPLICATIONS, 2024, 卷号: 243, 页码: 20
作者: He, Shuang; Tian, Jia; Hao, Lina; Zhang, Sen; Tian, Qingjiu

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收藏 | 浏览/下载：26/0 | 提交时间：2024/02/18

Frequency domain Feature extraction Cross-domain fusion Hyperspectral image classification

MFFENet and ADANet: a robust deep transfer learning method and its application in high precision and fast cross-scene recognition of earthquake-induced landslides 期刊论文 OAI收割
LANDSLIDES, 2022, 页码: 31
作者: Xu, Qingsong; Ouyang, Chaojun; Jiang, Tianhai; Yuan, Xin; Fan, Xuanmei

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收藏 | 浏览/下载：69/0 | 提交时间：2022/05/23

Earthquake-induced landslide recognition Deep learning Unsupervised domain adaptation Multi-scale Feature Fusion with Encoder-decoder Network (MFFENet) Adversarial Domain Adaptation Network (ADANet) Landslide spatial analysis

Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation 期刊论文 OAI收割
IEEE Trans. Pattern Anal. Machine Intell., 2019, 卷号: 41, 期号: 5, 页码: 1027-1042
作者: Jian Liang; Ran He; Zhenan Sun; Tieniu Tan

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收藏 | 浏览/下载：65/0 | 提交时间：2019/06/10

Unsupervised Domain Adaptation Domain-invariant Projection Class-clustering Sampling-and-fusion

A multi-layer deep fusion convolutional neural network for sketch based image retrieval 期刊论文 OAI收割
NEUROCOMPUTING, 2018, 卷号: 296, 页码: 23-32
作者: Yu, Deng; Liu, Yujie; Pang, Yunping; Li, Zongmin; Li, Hua

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收藏 | 浏览/下载：31/0 | 提交时间：2019/12/10

Cross-domain retrieval Multi-layer modeling Deep feature fusion

Infrared and visual image fusion using LNSST and an adaptive dual-channel PCNN with triple-linking strength 期刊论文 OAI收割
Neurocomputing, 2018, 卷号: 310, 页码: 135-147
作者: Cheng, B. Y.; Jin, L. X.; Li, G. N.

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收藏 | 浏览/下载：31/0 | 提交时间：2019/09/17

LNSST ATD-PCNN Image fusion Singular value decomposition Auxiliary linking strength Triple-linking strength sparse representation shearlet transform multi-focus feature-extraction neural-network domain algorithm decomposition Computer Science

A novel fusion framework of visible light and infrared images based on singular value decomposition and adaptive DUAL-PCNN in NSST domain 期刊论文 OAI收割
Infrared Physics & Technology, 2018, 卷号: 91, 页码: 153-163
作者: Cheng, B. Y.; Jin, L. X.; Li, G. N.

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收藏 | 浏览/下载：35/0 | 提交时间：2019/09/17

NSST ADS-PCNN Image fusion Singular value decomposition Local structure information operator Linking strength shearlet transform feature-extraction nsct domain algorithm scheme Instruments & Instrumentation Optics Physics

General fusion method for infrared and visual images via latent low-rank representation and local non-subsampled shearlet transform 期刊论文 OAI收割
Infrared Physics & Technology, 2018, 卷号: 92, 页码: 68-77
作者: Cheng, B. Y.; Jin, L. X.; Li, G. N.

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收藏 | 浏览/下载：27/0 | 提交时间：2019/09/17

Latent low-rank representation Local non-subsampled shearlet transform Salient features Image fusion visible-light contourlet transform feature-extraction nsct domain algorithm pcnn nsst Instruments & Instrumentation Optics Physics

Study of image motion compensation in spectral imaging system 期刊论文 OAI收割
Proceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Large Mirrors and Telescopes, 2016, 卷号: 9682, 页码: 968217
作者: Li, Zhijun; Chen, Xing Long

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收藏 | 浏览/下载：32/0 | 提交时间：2018/06/14

Data Fusion Error Compensation Frequency Domain Analysis Imaging Systems Jitter Manufacture Mirrors Motion Compensation Optical Systems Optical Testing Spectroscopy Telescopes

Expression, purification and preliminary characterization of glucagon receptor extracellular domain 期刊论文 OAI收割
PROTEIN EXPRESSION AND PURIFICATION, 2013, 卷号: 89, 期号: 2, 页码: 232-240
Wu, Lili; Zhai, yujia; 翟宇佳; Lu, Jiuwei; Wang, Qinghua; Sun, Fei; 孙飞

收藏 | 浏览/下载：25/0 | 提交时间：2013/12/24

Glucagon receptor Extracellular domain MBP fusion DsbC co-expression Baculovirus expression Isothermal titration calorimetry

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：77/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.